Senior Data Platform Engineer in United States at Jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Platform Engineer based in the United States.

Join a growing technology team building the data foundation behind an AI-powered platform serving thousands of boutique fitness businesses. As the first Data Platform Engineer, you will take ownership of the infrastructure that collects, organizes, governs, and delivers trusted data across products and internal teams. You will help make reporting faster and more reliable while ensuring that business metrics remain consistent, traceable, and secure. Working closely with Data, Analytics, and R&D teams, you will shape the architecture supporting customer insights, operational intelligence, and AI-powered experiences. You will also establish the foundations that allow employees and AI agents to access reliable information independently and responsibly. This is an opportunity to define a critical platform from the ground up and influence how data creates value across an expanding SaaS ecosystem.

You will own the design, development, and continuous improvement of the data platform, ensuring that it delivers accurate, accessible, secure, and cost-effective information for both people and AI-driven applications.

Data Platform Reliability and Performance

• Build and maintain the data infrastructure supporting reporting and data-driven products used by more than 5,000 gyms.

• Ensure reporting data is fresh, accurate, and performant, enabling gym owners to make timely business decisions.

• Establish data lineage and traceability so that key business metrics, including executive and Board-level reporting, can be tracked back to their sources.

• Define and monitor data freshness, availability, and reliability standards.

• Participate in incident response, investigate root causes, and implement improvements to prevent recurring issues.

Data Integration and Architecture

• Integrate product, payment, and third-party data to create a comprehensive view of customer and member journeys.

• Partner with software engineering teams to ensure new features and integrations generate clean, structured, and reliable data from the outset.

• Establish data contracts and identify breaking changes before they reach production.

• Simplify data architecture by reducing unnecessary pipelines and tools, clarifying ownership, and improving cost visibility.

• Manage and optimize ingestion workflows, orchestration systems, and custom data pipelines.

Privacy, Security, and Governance

• Design and implement data access controls that define what employees and AI agents can access.

• Maintain comprehensive audit trails for data access and usage.

• Establish safeguards that prevent AI systems from modifying or bypassing critical security and governance controls.

• Implement processes for handling privacy requests, including data retention and deletion, without compromising financial reporting or audit requirements.

• Support compliance with applicable privacy and security frameworks.

AI-Ready Data Infrastructure

• Build the data foundations required by AI-powered assistants, customer intelligence features, and reporting tools.

• Provide AI applications with the context and reliable information needed to generate relevant, personalized insights.

• Develop clear, consistent business definitions and semantic layers so that metrics have the same meaning across dashboards, conversational interfaces, and executive reporting.

• Enable teams across the organization to answer their own questions through AI, with appropriate controls over access, usage, and costs.

• Explore retrieval, evaluation, and governed data-access approaches that make AI agents more reliable and useful.

Documentation and Continuous Improvement

• Develop and maintain technical documentation, operational runbooks, data quality checks, and monitoring alerts.

• Establish processes that make platform operations understandable and manageable by other team members.

• Share architectural decisions and technical tradeoffs clearly with engineering and business stakeholders.

The ideal candidate is a hands-on data infrastructure engineer who has owned production systems end to end and can combine strong engineering fundamentals with a practical understanding of data governance, reliability, and AI-enabled workflows.

Technical Experience

• At least 5 years of experience in data engineering, data platform engineering, data infrastructure, or software engineering, with ownership of production infrastructure from design through operation.

• Experience working in a B2B SaaS or payments environment is preferred.

• Strong experience with cloud data warehouses, including performance optimization, cost management, access controls, role-based access control (RBAC), data masking, and schema design.

• Advanced proficiency in Python and SQL, supported by strong software engineering practices.

• Experience with automated testing, CI/CD, code reviews, and Infrastructure as Code tools such as Terraform.

• Hands-on experience deploying, maintaining, and operating workflow orchestration platforms such as Airflow or Dagster in production.

• Experience managing data ingestion tools such as Hevo or Fivetran, as well as building custom pipelines when managed solutions are insufficient.

• Practical experience with at least one of the following: change data capture (CDC), streaming, or event-driven architectures.

• Experience implementing data quality checks, monitoring, and alerting using tools such as Monte Carlo, Elementary, or custom-built solutions.

• Ability to create operational runbooks that enable others to investigate and resolve data issues.

• Daily use of AI tools, including at least one AI-powered workflow used by others and a validation mechanism to detect inaccurate outputs.

Additional Technical Knowledge

• Familiarity with real-time analytics databases such as ClickHouse and an understanding of when to use them instead of a traditional data warehouse.

• Knowledge of privacy and governance requirements under GDPR, CCPA, or PCI, including access management, auditing, retention, and deletion.

• Experience working with TypeScript codebases and collaborating with product engineers responsible for generating data.

• Interest or experience in data architecture for AI, including semantic layers, retrieval, evaluations, and governed access for AI agents.

• Understanding of data contracts and collaboration practices between data producers and platform teams.

• Familiarity with event platforms or customer data platforms such as Segment, RudderStack, Snowplow, or PostHog.

• Knowledge of identity resolution across product, billing, and CRM data.

Professional Skills

• Genuine curiosity about the customers and end users represented by the data.

• Strong problem-solving skills and the ability to investigate upstream causes of data inconsistencies.

• Clear communication skills, including the ability to explain technical tradeoffs in accessible terms.

• Pragmatic decision-making and the confidence to identify limitations, explain risks, and recommend when a solution is not yet ready.

• A proactive approach to continuous improvement, with an emphasis on documentation, monitoring, and operational reliability.

• Ability to work collaboratively across Data, Analytics, Product, and Engineering teams.

• Equity Incentive Plan.

• Flexible paid time off and public holidays.

• Healthcare coverage, including medical, dental, and vision plans.

• Company-sponsored 401(k) retirement plan with employer match.

• Life insurance.

• Family leave, including maternity and paternity leave.

• Work-from-home stipend.

• Wellness stipend.